SoroushVahidi/scidocs huggingface dataset:
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them
(2013)

The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling
(2017)

Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.
Sign language recognition with recurrent neural network using human keypoint detection
(2018)

We study the sign language recognition problem which is to translate the meaning of signs from visual input such as videos. It is well-known that many problems in the field of computer vision require a huge amount of dataset to train deep neural network models. We introduce the KETI sign language dataset which consists of 10,480 videos of high resolution and quality. Since different sign languages are used in different countries, the KETI sign language dataset can be the starting line for further research on the Korean sign language recognition.
 Using the sign language dataset, we develop a sign language recognition system by utilizing the human keypoints extracted from face, hand, and body parts. The extracted human keypoint vector is standardized by the mean and standard deviation of the keypoints and used as input to recurrent neural network (RNN). We show that our sign recognition system is robust even when the size of training data is not sufficient. Our system shows 89.5% classification accuracy for 100 sentences that can be used in emergency situations.
Enhancement of Images Using Histogram Processing Techniques
(2011)

Image enhancement is a mean as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Image enhancement processes consist of a collection of techniques that seek to improve the visual appearance of an image or to convert the image to a form better suited for analysis by a human or machine. Many images such as medical images, remote sensing images, electron microscopy images and even real life photographic pictures, suffer from poor contrast. Therefore it is necessary to enhance the contrast.The purpose of image enhancement methods is to increase image visibility and details. Enhanced image provide clear image to eyes or assist feature extraction processing in computer vision system. Numerous enhancement methods have been proposed but the enhancement efficiency, computational requirements, noise amplification, user intervention, and application suitability are the common factors to be considered when choosing from these different methods for specific image processing application.
Insights into Alzheimer disease pathogenesis from studies in transgenic animal models
(2011)

Alzheimer disease is the most common cause of dementia among the elderly, accounting for ~60-70% of all cases of dementia. The neuropathological hallmarks of Alzheimer disease are senile plaques (mainly containing p-amyloid peptide derived from amyloid precursor protein) and neurofibrillary tangles (containing hyperphosphorylated Tau protein), along with neuronal loss. At present there is no effective treatment for Alzheimer disease. Given the prevalence and poor prognosis of the disease, the development of animal models has been a research priority to understand pathogenic mechanisms and to test therapeutic strategies. Most cases of Alzheimer disease occur sporadically in people over 65 years old, and are not genetically inherited. Roughly 5% of patients with Alzheimer disease have familial Alzheimer disease--that is, related to a genetic predisposition, including mutations in the amyloid precursor protein, presenilin 1, and presenilin 2 genes. The discovery of genes for familial Alzheimer disease has allowed transgenic models to be generated through the overexpression of the amyloid precursor protein and/or presenilins harboring one or several mutations found in familial Alzheimer disease. Although none of these models fully replicates the human disease, they have provided valuable insights into disease mechanisms as well as opportunities to test therapeutic approaches. This review describes the main transgenic mouse models of Alzheimer disease which have been adopted in Alzheimer disease research, and discusses the insights into Alzheimer disease pathogenesis from studies in such models. In summary, the Alzheimer disease mouse models have been the key to understanding the roles of soluble b-amyloid oligomers in disease pathogenesis, as well as of the relationship between p-amyloid and Tau pathologies.
Centrality based Document Ranking
(2014)

In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially, Lucene is used to retrieve top fifty documents that are relevant to the query and then our proposed approach is applied on these retrieved documents to rerank them. Experimental results show that our approach did not perform well as the documents retrieved by Lucene are not among the top 50 documents in the Gold Standard.
Proceso de Valoración para la Mejora de Procesos Software en Pequeñas Organizaciones
(2008)

1 Grupo IDIS Facultad de Ingeniería Electrónica y Telecomunicaciones Universidad del Cauca Calle 5 No. 4 – 70. Popayán, Cauca, Colombia. fjpino@unicauca.edu.co. Web: http://www.unicauca.edu.co/idis/ 2 Grupo Alarcos Escuela Superior de Informática Universidad Castilla-La Mancha Paseo de la Universidad 4, Ciudad Real, España. {Felix.Garcia, Mario.Piattini}@uclm.es. Web: http://alarcos.inf-cr.uclm.es/
Exploiting Structure in Policy Construction
(1995)

Markov decision processes (MDPs) have recently been appbed to the problem of modeling decisiontheoretic planning While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable We present an algorithm, called structured policy Iteration (SPI), that con­ structs optimal policies without explicit enumera­ tion of the state space The algorithm retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but ex­ ploits the variable and prepositional independencies reflected in a temporal Bayesian network represen tation of MDPs The principles behind SPI can be applied to any structured representation of stochas­ tic actions, pobcies and value functions, and the algorithm itself can be used in conjunction with re­ cent approximation methods
Machine Learning with World Knowledge: The Position and Survey
(2017)

Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.
Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization
(2014)

In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
(2016)

Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can successfully characterize the temporal correlation, thus exhibits superior capability for time series tasks. Traffic flow data have plentiful characteristics on both time and space domain. However, applications of CNN and LSTM approaches on traffic flow are limited. In this paper, we propose a novel deep architecture combined CNN and LSTM to forecast future traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow. Given those meaningful features, the feature-level fusion is performed to achieve short-term traffic flow forecasting. The proposed CLTFP is compared with other popular forecasting methods on an open datasets. Experimental results indicate that the CLTFP has considerable advantages in traffic flow forecasting. in additional, the proposed CLTFP is analyzed from the view of Granger Causality, and several interesting properties of traffic flow and CLTFP are discovered and discussed . Traffic flow forecasting, Convolutional neural network, Long short-term memory, Feature-level fusion
Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques
(2015)

EEG is a record of brain activity from various sites of the brain. Artefacts are unwanted noise signals in an EEG record. Classification of artefacts based on source of its generation like physiological artefacts and external artefacts. Body of the subjects are main source of Physiological artifacts, while external artefacts are from outside the body due to the environment or measuring devices. Recognition, identification and elimination of artifacts is an important process to minimize the chance of misinterpretation of EEG, not only for clinical and non-clinical fields such as brain computer interface, intelligent control system robotics etc. This paper classifies the artefacts from the database collected at Dr. R. N. Cooper Mun. General Hospital Mumbai India.
Autonomia: an autonomic computing environment
(2003)

The prolifeation of Internet technologies, services and devices, have made the current networked system designs, and management tools incapable of designing reliable, secure networked systems and services. In fact, we have reached a level of complexity, heterogeneity, and a rapid change rate that our information infiastructure is becoming unmanageable and insecure. This had led researchers to consider alternative designs and management techniques that are based on strategies used by biological systems to deal with complexity, heterogeneity and uncertainty. The approach is referred to as autonomic computing. An autonomic computing system is the system that has the capabilities of being sevdefining, self-healing, self-configwing, self-optimizing, etc. In this paper, we present our approach to implement an autonomic computing infastructure, Autonomia that provides dynamically programmable control and management services to support the development and deployment of smart (intelligent) applications. The A UTONOMIA environment provides the application developers with all the tools required to specifj, the appropriate control and management schemes to maintain any quality of service requirement or application attribute/firnctionality (e.g., perjormance, fault, security, etc.) and the core autonomic middleware services to maintain the autonomic requirements of a wide range of network applications and services. We have successfully implemented a proof-of-concept prototype system that can support the self-configuring, self-deploying and selfhealing of any networked application.
SnapApp: Reducing Authentication Overhead with a Time-Constrained Fast Unlock Option
(2016)

We present SnapApp, a novel unlock concept for mobile devices that reduces authentication overhead with a time-constrained quick-access option. SnapApp provides two unlock methods at once: While PIN entry enables full access to the device, users can also bypass authentication with a short sliding gesture ("Snap"). This grants access for a limited amount of time (e.g. 30 seconds). The device then automatically locks itself upon expiration. Our concept further explores limiting the possible number of Snaps in a row, and configuring blacklists for app use during short access (e.g. to exclude banking apps). We discuss opportunities and challenges of this concept based on a 30-day field study with 18 participants, including data logging and experience sampling methods. Snaps significantly reduced unlock times, and our app was perceived to offer a good tradeoff. Conceptual challenges include, for example, supporting users in configuring their blacklists.
Towards Conversational Recommender Systems
(2016)

People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, most such systems behave very differently from a human when asked for a recommendation. The goal of this paper is to begin to reduce this gap. In particular, humans can quickly establish preferences when asked to make a recommendation for someone they do not know. We address this cold-start recommendation problem in an online learning setting. We develop a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences. Taking advantage of latent structure in the recommendation space using a probabilistic latent factor model, our experiments with both synthetic and real world data compare different types of feedback and question selection strategies. We find that our framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions. Our results demonstrate dramatic benefits of starting from offline embeddings, and highlight the benefit of bandit-based explore-exploit strategies in this setting.
An autonomous flyer photographer
(2016)

In this paper we explore the combination of a latest generation mobile device and a micro quadrotor platform to perform indoor autonomous navigation for the purpose of autonomous photography. We use the Yellowstone tablet from Google's Tango project [1], equipped with onboard, fully integrated sensing platform and with significant computational capability. To the best of our knowledge we are the first to exploit the Google's Tango tablet as source of pose estimate to control the quadrotor's motion. Using the tablet's onboard camera the system is able to detect people and generate a desired pose that the quadrotor will have to reach in order to take a well framed picture of the detected subject. The experimental results and live video demonstrate the capabilities of the autonomous flying robot photographer using the system described throughout this manuscript.
Conservative surgical management of subungual (matrix derived) melanoma: report of seven cases and literature review.
(2011)

BACKGROUND
Subungual melanoma (SUM) is a rare entity, comprising approximately 0·7-3·5% of all melanoma subtypes. SUM histopathologically belongs to the acral lentiginous pathological subtype of malignant melanoma. Its diagnosis is helped by dermoscopy but pathological examination of doubtful cases is required. Classical management of SUM is based on radical surgery, namely distal phalanx amputation. Conservative treatment with nonamputative wide excision of the nail unit followed by a skin graft has been insufficiently reported in the medical literature even though it is performed in many centres.


OBJECTIVES
To report a series of patients with in situ or minimally invasive SUM treated by conservative surgery, to investigate the postoperative evolution and to evaluate the outcome with a review of the literature.


METHODS
We performed a retrospective extraction study from our melanoma register of all patients with in situ and minimally invasive SUM treated with conservative surgery in the University Hospital Department of Dermatology, Lyon, France from 2004 to 2009. The patient demographics, disease presentation, delay to diagnosis, histopathology and postoperative evolution were reviewed.


RESULTS
Seven cases of SUM treated as such were identified in our melanoma database. All cases had a clinical presentation of melanonychia striata. The mean delay to diagnosis was 2years. Surgical excision of the entire nail unit with a 5-10mm safety margin without bone resection followed by full-thickness skin graft taken from the arm was performed in all cases. No recurrence was observed with a mean follow-up of 45months. Functional results were found satisfactory by all patients and their referring physicians. Sixty-two other cases have been found in the literature and are also discussed.


CONCLUSIONS
Conservative surgical management in patients with in situ or minimally invasive SUM is a procedure with good cosmetic and functional outcome and, in our cases as well as in the literature, the prognosis is not changed.
Examining The Adoption Of Human Resource Information System In The Context Of Saudi Arabia
(2014)

The aim of this paper is to examine the factors influencing the adoption of the human resource information system (HRIS) in the Saudi context. A few researches have looked at the adoption of HRIS, but none of these studies have examined the adoption of such a system from the users’ perspective in general and Saudi Arabia (SA) in particular. This study has developed a model that integrates service quality with perceived usefulness and perceived ease of use to investigate the factors affecting the users’ adoption of HRIS. The model was empirically tested using the structure equation modelling by employing data collected from a survey of HRIS users. The findings and discussion presented in this study will help the Saudi organisations to understand the current status of their HRIS to improve it and benefit from it. This study provides guidelines for its implementation for theory and practices, limitations and future directions.
Exploring the role of robotic kissing “Kissenger” in digital communication through alan turing's imitation game
(2017)

With advances in robotics and artificial intelligence, and increasing prevalence of commercial social robots and chatbots, humans are communicating with artificially intelligent machines for various applications in a wide range of settings. Intimate touch is crucial to relationships forming and emotional bonding, however it remains as a missing element in digital communication between humans, as well as humans and machines. A kissing machine, Kissenger, is built to transmit haptic kissing sensations in a communication network. In order to investigate the role of robotic kissing using the Kissenger device in digital communication, we conducted a modified version of the Imitation Game described by Alan Turing by including the use of the kissing machine. The study consists of 4 rounds, in which participants communicate with two players, a male and a female, using online chatrooms with the goal to identify the female player. In the last two rounds, the male player is replaced with a chatbot with female personas. Participants are told to remotely kiss the players using the kissing device at least once during the chat in the second and fourth round, but not in the first and third round. Results show that robotic kissing has no effect on the winning rates of the male and female players during human-human communication, but it increases the winning rate of the female player when a chatbot is involved in the game.